Kong, Kezhi

9 publications

ICLR 2024 On the Reliability of Watermarks for Large Language Models John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein

ICLR 2024 OpenTab: Advancing Large Language Models as Open-Domain Table Reasoners Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis

ICML 2023 GOAT: A Global Transformer on Large-Scale Graphs Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

CVPR 2022 Robust Optimization as Data Augmentation for Large-Scale Graphs Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

NeurIPSW 2021 A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

ICML 2021 Data Augmentation for Meta-Learning Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein

NeurIPS 2021 GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

AAAI 2021 SHOT-VAE: Semi-Supervised Deep Generative Models with Label-Aware ELBO Approximations Haozhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen

NeurIPS 2021 VQ-GNN: A Universal Framework to Scale up Graph Neural Networks Using Vector Quantization Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein